March 28, 2016

Set up & Configuration

Datasets, presentation, source code

Load data

movies <- read.csv("data/movies.csv", stringsAsFactors = FALSE)

ggplot2

ggplot2

  • ggplot2 is a plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and none of the bad parts.

  • It takes care of many of the fiddly details that make plotting a hassle (like drawing legends) as well as providing a powerful model of graphics that makes it easy to produce complex multi-layered graphics.


Source: http://ggplot2.org/

Why ggplot2?

  • Statistical: Visualize \(Y \sim X | Z\) with ease

  • Technical: Human readable syntax

  • Bonus: Aesthetically pleasing

With ggplot2

ggplot(data = movies, aes(x = critics_score, y = audience_score, color = title_type)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE)

With base R

plot(y = movies$audience_score, x = movies$critics_score, 
     col = factor(movies$title_type))

doc <- movies[movies$title_type == "Documentary", ]
ff <- movies[movies$title_type == "Feature Film", ]
tv <- movies[movies$title_type == "TV Movie", ]

m_doc <- lm(audience_score ~ critics_score, data = doc)
m_ff <-  lm(audience_score ~ critics_score, data = ff)
m_tv <-  lm(audience_score ~ critics_score, data = tv)

abline(m_doc, col = 1)
abline(m_ff, col = 2)
abline(m_tv, col = 3)

legend("topleft", levels(factor(movies$title_type)), 
       col = c(1,2,3), lty = 1)

With base R

Grammar of Graphics

The Grammar of Graphics

  • Visualisation concept created by Wilkinson (1999)
    • to define the basic elements of a statistical graphic
  • Adapted for R by Wickham (2009) who created the ggplot2 package
    • consistent and compact syntax to describe statistical graphics
    • highly modular as it breaks up graphs into semantic components
  • Is not a guide which graph to choose and how to convey information best!


Source: https://rpubs.com/timwinke/ggplot2workshop

The Grammar of Graphics - Terminology

A statistical graphic is a…

  • mapping of data
  • to aesthetic attributes (color, size, xy-position)
  • using geometric objects (points, lines, bars)
  • with data being statistically transformed (summarised, log-transformed)
  • and mapped onto a specific facet and coordinate system

Audience score vs. critics score

  • Which data is used as an input?
  • What geometric objects are chosen for visualization?
  • What variables are mapped onto which attributes?
  • What type of scales are used to map data to aesthetics?
  • Are the variables statistically transformed before plotting?

Audience score vs. critics score - code

ggplot(data = movies, aes(x = audience_score, y = critics_score)) +
  geom_point()

Altering features

  • How did the plot change?
  • Are these changes based on data (i.e. can be mapped to variables in the dataset) or changes in preferences for geometric objects?

Altering features - code

ggplot(data = movies, aes(x = audience_score, y = critics_score)) +
  geom_point(alpha = 0.5, color = "blue")

Faceting

  • How did the plot change?
  • Are these changes based on data (i.e. can be mapped to variables in the dataset) or changes in preferences for geometric objects?

Faceting - code

ggplot(data = movies, aes(x = audience_score, y = critics_score, color = genre)) +
  geom_point(alpha = 0.5) +
  facet_grid(. ~ title_type)

More faceting

ggplot(data = movies, aes(x = audience_score, y = critics_score, color = genre)) +
  geom_point(alpha = 0.5) +
  facet_grid(audience_rating ~ title_type)

Anatomy of ggplots

ggplot(data = [dataframe], aes(x = [var_x], y = [var_y], 
       color = [var_for_color], fill = [var_for_fill], 
       shape = [var_for_shape])) +
    geom_[some_geom] +
    ... # other options

Various plots

Histograms

ggplot(data = movies, aes(x = audience_score)) +
  geom_histogram(binwidth = 5)

Boxplots

ggplot(data = movies, aes(y = audience_score, x = genre)) +
  geom_boxplot()

Boxplots - axis formatting

ggplot(data = movies, aes(y = audience_score, x = genre)) +
  geom_boxplot() +
  theme(axis.text.x=element_text(angle = 45, hjust = 1))

Density plots - border color

ggplot(data = movies, aes(x = runtime, color = audience_rating)) +
  geom_density()

Density plots - fill color

ggplot(data = movies, aes(x = runtime, fill = audience_rating)) +
  geom_density()

Density plots - fill color, with alpha

ggplot(data = movies, aes(x = runtime, fill = audience_rating)) +
  geom_density(alpha = 0.5)

Scatter plots

ggplot(data = movies, aes(x = imdb_rating, y = audience_score)) +
  geom_point(alpha = 0.5)

Smoothing

ggplot(data = movies, aes(x = imdb_rating, y = audience_score)) +
  geom_point(alpha = 0.5) +
  geom_smooth()

Smoothing - lm

ggplot(data = movies, aes(x = imdb_rating, y = audience_score)) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm")

Barplots

ggplot(data = movies, aes(x = genre)) +
  geom_bar() +
  theme(axis.text.x=element_text(angle = 45, hjust = 1))

Segmented barplots

ggplot(data = movies, aes(x = genre, fill = audience_rating)) +
  geom_bar() +
  theme(axis.text.x=element_text(angle = 45, hjust = 1))

Segmented barplots - proportions

ggplot(data = movies, aes(x = genre, fill = audience_rating)) +
  geom_bar(position = "fill") +
  theme(axis.text.x=element_text(angle = 45, hjust = 1))

Dodged barplots

ggplot(data = movies, aes(x = genre, fill = audience_rating)) +
  geom_bar(position = "dodge") +
  theme(axis.text.x=element_text(angle = 45, hjust = 1))

Not on team theme_grey()?

ggplot(data = movies, aes(x = genre, fill = audience_rating)) +
  geom_bar(position = "dodge") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

More ggplot2 resources

Exercise

Recereate the following plot. Hint: Add a labs() layer.

Shiny

Shiny

  • A web application framework for R with which you can easily turn your analyses into interactive web applications

  • No HTML, CSS, or JavaScript knowledge required

Set up

install.packages("shiny")
library(shiny)
  • The Shiny package has built-in examples that each demonstrate how Shiny works. Each example is a self-contained Shiny app.

Structure of a Shiny App

Shiny apps have two components:

  • a user-interface script: controls the layout and appearance of your app

  • a server script: contains the instructions that your computer needs to build your app

Give it a try

runExample("01_hello")

Various control widgets

Live demo

Shiny applications not supported in static R Markdown documents